Applied Natural Language Processing



Prof. Ramaseshan R

Chennai Mathematical Institute

*Additional GST and optional Exam fee are applicable.

SKU: Chennai Mathematical Institute Category:


Natural Language Processing (NLP) is an important area of Artificial Intelligence concerned with the processing and understanding (NLU) of a human language. The goal of NLP and NLU is to process and harness information from a large corpus of text with very little manual intervention.
This course will introduce various techniques to find similar words using the context of surrounding words, build a Language model to predict the next word and generate sentences, encode every word in the vocabulary of the corpus into a vector form that represents its context and similar words and encode a sentence for machine translation and conversation purposes.
The course will help learners to gather sufficient knowledge and proficiency in probabilistic, Artificial Neural Network (ANN) and deep learning techniques for NLP.


Any interested learners


Essential – Algorithms, Python proficiency, elementary probability and statistics, Linear Algebra, basic understanding of machine learning


Prof. Ramaseshan R. He is currently working as a Visiting faculty at CMI and handling NLP. He has more than 30 years of experience in research and development, teaching, product development, information technology, innovation, and convergence.



Additional information


Chennai Mathematical Institute

Total hours


Certification Process

1. Join the course
Learners may pay the applicable fees and enrol to a course on offer in the portal and get access to all of its contents including assignments. Validity of enrolment, which includes access to the videos and other learning material and attempting the assignments, will be mentioned on the course. Learner has to complete the assignments and get the minimum required marks to be eligible for the certification exam within this period.

COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 3000 + GST

2. Watch Videos+Submit Assignments
After enrolling, learners can watch lectures and learn and follow it up with attempting/answering the assignments given.

3. Get qualified to register for exams
A learner can earn a certificate in the self paced course only by appearing for the online remote proctored exam and to register for this, the learner should get minimum required marks in the assignments as given below:

Assignment score = Score more than 50% in at least 9/12 assignments.
Exam score = 50% of the proctored certification exam score out of 100
Only the e-certificate will be made available. Hard copies will not be dispatched.”

4. Register for exams
The certification exam is conducted online with remote proctoring. Once a learner has become eligible to register for the certification exam, they can choose a slot convenient to them from what is available and pay the exam fee. Schedule of available slot dates/timings for these remote-proctored online examinations will be published and made available to the learners.

EXAM FEE: The remote proctoring exam is optional for a fee of Rs.1500 + GST. An additional fee of Rs.1500 will apply for a non-standard time slot.

5. Results and Certification
After the exam, based on the certification criteria of the course, results will be declared and learners will be notified of the same. A link to download the e-certificate will be shared with learners who pass the certification exam.


Course Details

WEEK 1:   Introduction, terminologies, empirical rules
WEEK 2:   Word to Vectors
WEEK 3:   Probability and Language Model
WEEK 4:   Neural Networks for NLP
WEEK 5:   Distributed word vectors (word embeddings)
WEEK 6:   Recurrent Neural Network, Language Model
WEEK 7:   Statistical Machine Translation
WEEK 8:   Statistical Machine Translation, Neural Machine Translation
WEEK 9:   Neural Machine Translation
WEEK 10: Conversation Modeling, Chat-bots, dialog agents, Question Processing
WEEK 11: Information Retrieval tasks using Neural Networks- Learn to Rank, Understanding Phrases, analogies
WEEK 12Spelling Correction using traditional and Neural networks, end notes


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